Pipeline analysis plays an essential role in ensuring the structural integrity and safe operation of these critical components. Finite Element Analysis is the commonly used method for assessing stress distribution in pipelines. However, it can be computationally expensive and time-consuming, necessitating alternative approaches. The use of machine learning offers a promising alternative by providing a faster and more efficient approach to predicting stress distribution. This paper shows a prototype focused on the development of a stress distribution surrogate prediction model using an encoder-decoder-based convolutional neural network to analyze the stress distribution of a 3D cylindrical pipe. The related works and methodology are discussed. Then, the results of the study are presented, followed by main conclusions and ideas for future work.

This paper presents and discusses a comparative study of three major clustering categories namely Hierarchical-based, Iterative mode-based and Partition-based in analyzing and prioritizing Mobile Augmented reality (MAR) Learning (MAR-learning) usability data. This paper first discusses the related works in usability and clustering before moving on to the identification of gaps that can be addressed through experimentation. This paper will then propose a research methodology to measure four common clustering techniques on MAR-learning usability data. The paper will then discourse comparative results showing how Mini-batch K-means to be an ideal technique within the experimental setup. The paper will then present important research highlights, discussion, conclusion and future works.

This paper presents the on-going work of MyDNAmark crowdsourcing biodiversity mobile application which enable the researchers and public alike to collaboratively record the species information and observation. The MyDNAmark application utilizes the Cordova API and various other APIs such as RESTful and Google Map to provide a means to display the collected species data, addition of new species data to server, get the geolocation of user, etc. A key feature is the offline capability which allows the user to save the species observation data locally when there are no Internet connection, and the local data is submitted to server once there are Internet connection. The capabilities of MyDNAmark is demonstrated through real end-user scenarios with the participation of fellow researchers working in the biological fields.

This paper presents and discusses an empirical work of using machine learning K-means clustering algorithm in analyzing and processing Mobile Augmented Reality (MAR) learning usability data. This paper first discusses the issues within usability and machine learning spectrum, then explain in detail a proposed methodology approaching the experiments conducted in this research. This contributes in providing empirical evidence on the feasibility of K-means algorithm through the discreet display of preliminary outcomes and performance results. This paper also proposes a new usability prioritization technique that can be quantified objectively through the calculation of negative differences between cluster centroids. Towards the end, this paper will discourse important research insights, impartial discussions and future works.

This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative clustering and K-means clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data. This paper contributes in presenting evidences showing K-means as the better performing clustering algorithm when compared to Hierarchical Agglomerative when implemented on the usability datasets. The results shown has contradicted with some recent studies claiming otherwise, and the findings have created more research gaps pertaining the combined utilization of machine learning and usability analysis.

This paper presents fractions of research outcome from a bigger project involving machine learning, Hierarchical Agglomerative Clustering (HAC) Algorithms on usability data gathered through performance and self-reported data. This paper highlights the common problems in usability studies where the conventional analysis was frequently utilized while prioritizing usability issues. The utilization of clustering techniques is limited in the area of this study. A previous publication has shown how HAC was used in clustering usability problems in Mobile Augmented Reality (MAR) learning applications. However, there has not been a triangulation effort to confirm the first gathered results due to small datasets. This research presents a methodology adopted from previous studies in confirming earlier usability analysis results. The experiments found consistent evidence approving the feasibility of HAC in clustering and prioritizing Usability performance and self-reported data.

This paper highlights the current literatures in usability studies, performance metrics, self-reported metrics and hierarchical agglomerative clustering algorithms. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to study comparatively feature selection based on performance and self-reported usability data. This paper will highlight methods used to compare the feasibility and performance of hierarchical agglomerative clustering algorithms on both performance and self-reported data. The results of the experiment will then be presented and discussed before proceeding to the conclusion and future works of this study.

This paper highlights the current literatures in usability studies, performance metrics and machine learning algorithm. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to attend to the issues of machine learning and usability. An experiment is proposed to compare the efficiency results in between data consistency, correlation between performance metrics and self-reported metrics of a Mobile Augmented Reality learning application. The methodology proposes hierarchical agglomerative clustering technique as a solution in differentiating usability issues according to priority in order to help with usability re-engineering decisions. This paper proposes two objectives through the proposed framework and present evidence on how to achieve them. Lastly, this paper will discuss the results, conclusion and future works of the proposed study.

This paper describes the process of developing a visualisation system with the use of Game Engine. A bottom up approach was used in developing the system, which allows process alignment with the Game Engine structure. The approach allows large abstract data processing, which are channelled through the components of visual interfaces and supported with relational data input structure. The developed system provides automation in much of the pipeline in producing dynamic visualisation selections. As case studies, the UK Research Assessment Exercise (RAE2008) and 2010 World Oil Reserves datasets were used to demonstrate the usability of the Game Engine based visualisation system.

In written and spoken communications, figures of speech (e.g., metaphors and synecdoche) are often used as an aid to help convey abstract or less tangible concepts. However, the benefits of using rhetorical illustrations or embellishments in visualization have so far been inconclusive. In this work, we report an empirical study to evaluate hypotheses that visual embellishments may aid memorization, visual search and concept comprehension. One major departure from related experiments in the literature is that we make use of a dual-task methodology in our experiment. This design offers an abstraction of typical situations where viewers do not have their full attention focused on visualization (e.g., in meetings and lectures). The secondary task introduces "divided attention", and makes the effects of visual embellishments more observable. In addition, it also serves as additional masking in memory-based trials. The results of this study show that visual embellishments can help participants better remember the information depicted in visualization. On the other hand, visual embellishments can have a negative impact on the speed of visual search. The results show a complex pattern as to the benefits of visual embellishments in helping participants grasp key concepts from visualization.